To use incense we first have to instantiate an experiment loader that will enable us to query the database for specific runs.
| targets_type | iteration | autoencoder_type | batch_size | artifacts | |
|---|---|---|---|---|---|
| exp_id | |||||
| 17 | Mnist | False | Over_dim_iteration | 256 | {'history_autoencoder_iteration': Artifact(nam... |
| 18 | Mnist | False | Over_dim_iteration | 128 | {'history_autoencoder_iteration': Artifact(nam... |
| 19 | Mnist | False | Over_dim_iteration | 64 | {'history_autoencoder_iteration': Artifact(nam... |
| 20 | Mnist | False | Over_dim_iteration | 32 | {'history_autoencoder_iteration': Artifact(nam... |
| 21 | 10_Targets | False | Over_dim_iteration | 256 | {'history_autoencoder_iteration': Artifact(nam... |
| 22 | 10_Targets | False | Over_dim_iteration | 128 | {'history_autoencoder_iteration': Artifact(nam... |
| 23 | 10_Targets | False | Over_dim_iteration | 64 | {'history_autoencoder_iteration': Artifact(nam... |
| 24 | 10_Targets | False | Over_dim_iteration | 32 | {'history_autoencoder_iteration': Artifact(nam... |
| 74 | Noisy | False | Over_dim_iteration | 256 | {'history_autoencoder': Artifact(name=history_... |
| 75 | Noisy | False | Over_dim_iteration | 128 | {'history_autoencoder': Artifact(name=history_... |
| targets_type | iteration | autoencoder_type | batch_size | artifacts | sort | |
|---|---|---|---|---|---|---|
| exp_id | ||||||
| 21 | 10_Targets | False | Over_dim_iteration | 256 | {'history_autoencoder_iteration': Artifact(nam... | 0 |
| 22 | 10_Targets | False | Over_dim_iteration | 128 | {'history_autoencoder_iteration': Artifact(nam... | 1 |
| 23 | 10_Targets | False | Over_dim_iteration | 64 | {'history_autoencoder_iteration': Artifact(nam... | 2 |
| 24 | 10_Targets | False | Over_dim_iteration | 32 | {'history_autoencoder_iteration': Artifact(nam... | 3 |
| 17 | Mnist | False | Over_dim_iteration | 256 | {'history_autoencoder_iteration': Artifact(nam... | 4 |
| 18 | Mnist | False | Over_dim_iteration | 128 | {'history_autoencoder_iteration': Artifact(nam... | 5 |
| 19 | Mnist | False | Over_dim_iteration | 64 | {'history_autoencoder_iteration': Artifact(nam... | 6 |
| 20 | Mnist | False | Over_dim_iteration | 32 | {'history_autoencoder_iteration': Artifact(nam... | 7 |
| 74 | Noisy | False | Over_dim_iteration | 256 | {'history_autoencoder': Artifact(name=history_... | 8 |
| 75 | Noisy | False | Over_dim_iteration | 128 | {'history_autoencoder': Artifact(name=history_... | 9 |
Red best overall, and also best of subset. Bes means for accuracy max, rest min. Green best of subset.
predictions_df_0
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.9848 | 0.9817 | 0.9757 | 0.9638 | 0.977 | 0.9759 | 0.9747 | 0.9725 | 0.9717 | 0.9703 |
| 1 | 0.9818 | 0.9763 | 0.9305 | 0.9384 | 0.9721 | 0.9695 | 0.9698 | 0.9654 | 0.9606 | 0.959 |
| 2 | 0.9817 | 0.9762 | 0.9162 | 0.9333 | 0.9604 | 0.9547 | 0.9525 | 0.9452 | 0.941 | 0.941 |
| 3 | 0.9817 | 0.9762 | 0.9065 | 0.9332 | 0.9427 | 0.937 | 0.9251 | 0.922 | 0.9195 | 0.9193 |
| 4 | 0.9817 | 0.9762 | 0.8987 | 0.9326 | 0.9193 | 0.9117 | 0.8902 | 0.8862 | 0.8965 | 0.8954 |
| 5 | 0.9817 | 0.9762 | 0.8731 | 0.9326 | 0.8922 | 0.8785 | 0.8458 | 0.8433 | 0.8709 | 0.8718 |
| 6 | 0.9817 | 0.9762 | 0.8701 | 0.9326 | 0.8575 | 0.8433 | 0.8019 | 0.7862 | 0.8476 | 0.8499 |
| 7 | 0.9817 | 0.9762 | 0.8701 | 0.9326 | 0.8167 | 0.8049 | 0.7578 | 0.7337 | 0.8256 | 0.8236 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.403742 | 0.397198 | 0.382812 | 0.388587 | 0.0292309 | 0.0339636 | 0.0360619 | 0.0344924 | 0.652874 | 0.653378 |
| 1 | 0.407135 | 0.405824 | 0.394443 | 0.400576 | 0.0463176 | 0.0545498 | 0.0602712 | 0.0584108 | 0.669197 | 0.670428 |
| 2 | 0.407422 | 0.40694 | 0.399517 | 0.405517 | 0.0675954 | 0.0800329 | 0.0893665 | 0.0863392 | 0.685468 | 0.687311 |
| 3 | 0.407448 | 0.407088 | 0.402149 | 0.407292 | 0.0904182 | 0.106922 | 0.119572 | 0.115036 | 0.70102 | 0.703296 |
| 4 | 0.407449 | 0.407112 | 0.404266 | 0.408127 | 0.113555 | 0.13365 | 0.149717 | 0.14319 | 0.715709 | 0.718164 |
| 5 | 0.407449 | 0.407114 | 0.407281 | 0.408358 | 0.136202 | 0.159653 | 0.178439 | 0.169983 | 0.729506 | 0.73188 |
| 6 | 0.407449 | 0.407115 | 0.40784 | 0.408404 | 0.158043 | 0.184622 | 0.206047 | 0.195145 | 0.742406 | 0.74451 |
| 7 | 0.407449 | 0.407115 | 0.407942 | 0.408414 | 0.178906 | 0.208454 | 0.232604 | 0.218491 | 0.754443 | 0.756112 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.265401 | 0.264138 | 0.26606 | 0.259944 | 0.0513701 | 0.0553199 | 0.0564859 | 0.0551706 | 0.378762 | 0.376407 |
| 1 | 0.265915 | 0.265573 | 0.267783 | 0.261732 | 0.0653205 | 0.0713066 | 0.0747933 | 0.0734418 | 0.389247 | 0.38766 |
| 2 | 0.265972 | 0.265772 | 0.269007 | 0.262598 | 0.0805217 | 0.0888189 | 0.0941929 | 0.092148 | 0.399146 | 0.398025 |
| 3 | 0.265977 | 0.265805 | 0.269806 | 0.262848 | 0.0953927 | 0.105831 | 0.112724 | 0.109869 | 0.408407 | 0.407519 |
| 4 | 0.265977 | 0.265811 | 0.270858 | 0.262942 | 0.109518 | 0.121862 | 0.1302 | 0.126343 | 0.416992 | 0.41614 |
| 5 | 0.265977 | 0.265811 | 0.271821 | 0.262971 | 0.122724 | 0.136864 | 0.146277 | 0.141473 | 0.424931 | 0.423939 |
| 6 | 0.265977 | 0.265811 | 0.27194 | 0.262975 | 0.135043 | 0.150866 | 0.161319 | 0.155292 | 0.432269 | 0.43101 |
| 7 | 0.265977 | 0.265811 | 0.271963 | 0.262974 | 0.146518 | 0.163961 | 0.175466 | 0.167862 | 0.439067 | 0.437445 |
predictions_df_10
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.9799 | 0.9763 | 0.9709 | 0.9589 | 0.9673 | 0.9672 | 0.9677 | 0.9618 | 0.9672 | 0.9668 |
| 1 | 0.9752 | 0.9711 | 0.9269 | 0.9348 | 0.9665 | 0.962 | 0.9618 | 0.9527 | 0.958 | 0.9564 |
| 2 | 0.9753 | 0.9708 | 0.9123 | 0.9297 | 0.9529 | 0.9477 | 0.9435 | 0.9359 | 0.9387 | 0.9382 |
| 3 | 0.9753 | 0.9708 | 0.9033 | 0.9286 | 0.933 | 0.928 | 0.9161 | 0.903 | 0.9138 | 0.9181 |
| 4 | 0.9753 | 0.9708 | 0.8932 | 0.9281 | 0.9095 | 0.9014 | 0.8793 | 0.863 | 0.8928 | 0.8955 |
| 5 | 0.9753 | 0.9708 | 0.872 | 0.9282 | 0.8784 | 0.8677 | 0.8331 | 0.8146 | 0.8702 | 0.8715 |
| 6 | 0.9753 | 0.9708 | 0.87 | 0.9282 | 0.8423 | 0.8292 | 0.7933 | 0.762 | 0.8482 | 0.8481 |
| 7 | 0.9753 | 0.9708 | 0.87 | 0.9282 | 0.8002 | 0.7843 | 0.7468 | 0.7097 | 0.825 | 0.8221 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.402999 | 0.395499 | 0.377159 | 0.385465 | 0.0399669 | 0.0433849 | 0.0458721 | 0.0470132 | 0.653984 | 0.654814 |
| 1 | 0.407379 | 0.405662 | 0.390463 | 0.399108 | 0.0549084 | 0.0616757 | 0.0677522 | 0.068914 | 0.67046 | 0.671816 |
| 2 | 0.407784 | 0.407174 | 0.396012 | 0.404569 | 0.0749327 | 0.0858256 | 0.0954971 | 0.0961543 | 0.686544 | 0.688534 |
| 3 | 0.407801 | 0.407408 | 0.398971 | 0.406481 | 0.0968927 | 0.11186 | 0.124867 | 0.124517 | 0.701879 | 0.70437 |
| 4 | 0.407802 | 0.407438 | 0.401572 | 0.407466 | 0.119405 | 0.137996 | 0.153959 | 0.152344 | 0.716393 | 0.71908 |
| 5 | 0.407802 | 0.407442 | 0.404292 | 0.407803 | 0.141745 | 0.163562 | 0.182658 | 0.178932 | 0.730049 | 0.732646 |
| 6 | 0.407802 | 0.407442 | 0.404807 | 0.407867 | 0.163358 | 0.188274 | 0.20948 | 0.203792 | 0.742828 | 0.745155 |
| 7 | 0.407802 | 0.407442 | 0.404918 | 0.407878 | 0.184035 | 0.212257 | 0.235622 | 0.226996 | 0.754794 | 0.756653 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.26548 | 0.264068 | 0.264819 | 0.259829 | 0.0614194 | 0.0638013 | 0.0651297 | 0.0661549 | 0.381173 | 0.378757 |
| 1 | 0.266103 | 0.265653 | 0.266559 | 0.261613 | 0.0721238 | 0.07678 | 0.080333 | 0.0811993 | 0.390269 | 0.38877 |
| 2 | 0.266171 | 0.265939 | 0.267765 | 0.26247 | 0.0857087 | 0.0928656 | 0.0982885 | 0.0986782 | 0.399767 | 0.398799 |
| 3 | 0.266174 | 0.265985 | 0.268615 | 0.262705 | 0.0996325 | 0.109077 | 0.11606 | 0.115747 | 0.40883 | 0.408122 |
| 4 | 0.266174 | 0.265992 | 0.269689 | 0.262814 | 0.113161 | 0.124596 | 0.132863 | 0.131755 | 0.417292 | 0.416612 |
| 5 | 0.266174 | 0.265993 | 0.270502 | 0.262856 | 0.126055 | 0.139256 | 0.148822 | 0.14653 | 0.425143 | 0.42431 |
| 6 | 0.266174 | 0.265993 | 0.270614 | 0.262864 | 0.138164 | 0.153065 | 0.163409 | 0.16003 | 0.432417 | 0.431306 |
| 7 | 0.266174 | 0.265993 | 0.270639 | 0.262864 | 0.149485 | 0.16618 | 0.177339 | 0.172386 | 0.439173 | 0.437674 |
predictions_df_20
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.9704 | 0.9683 | 0.9636 | 0.9526 | 0.9535 | 0.9545 | 0.9511 | 0.9423 | 0.9629 | 0.9607 |
| 1 | 0.9678 | 0.9638 | 0.9194 | 0.9285 | 0.9526 | 0.9538 | 0.9493 | 0.9403 | 0.9538 | 0.9523 |
| 2 | 0.9672 | 0.9638 | 0.9021 | 0.9215 | 0.9392 | 0.9355 | 0.9288 | 0.917 | 0.9351 | 0.9346 |
| 3 | 0.9671 | 0.9638 | 0.8946 | 0.9214 | 0.9185 | 0.9119 | 0.8972 | 0.8785 | 0.9135 | 0.9154 |
| 4 | 0.9671 | 0.9638 | 0.8856 | 0.92 | 0.8889 | 0.8804 | 0.8544 | 0.8346 | 0.8905 | 0.8926 |
| 5 | 0.9671 | 0.9638 | 0.8645 | 0.9197 | 0.8556 | 0.8436 | 0.8122 | 0.7825 | 0.8681 | 0.8676 |
| 6 | 0.9671 | 0.9638 | 0.8633 | 0.9196 | 0.8192 | 0.8077 | 0.7613 | 0.7232 | 0.8454 | 0.845 |
| 7 | 0.9671 | 0.9638 | 0.8633 | 0.9195 | 0.7772 | 0.77 | 0.7202 | 0.671 | 0.8223 | 0.8206 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.402029 | 0.393151 | 0.370475 | 0.382019 | 0.0519608 | 0.0546186 | 0.0576784 | 0.0621365 | 0.655572 | 0.656806 |
| 1 | 0.407717 | 0.405333 | 0.386412 | 0.397403 | 0.0652168 | 0.0709145 | 0.0776066 | 0.0823035 | 0.67222 | 0.67365 |
| 2 | 0.408289 | 0.407198 | 0.393006 | 0.40362 | 0.0842901 | 0.093695 | 0.104295 | 0.108502 | 0.688173 | 0.69007 |
| 3 | 0.40835 | 0.407518 | 0.396174 | 0.405981 | 0.105636 | 0.118774 | 0.132812 | 0.136141 | 0.703368 | 0.705605 |
| 4 | 0.408351 | 0.407564 | 0.399054 | 0.40727 | 0.127556 | 0.144172 | 0.161267 | 0.163401 | 0.717735 | 0.720078 |
| 5 | 0.408352 | 0.407569 | 0.401673 | 0.407816 | 0.149179 | 0.16899 | 0.189259 | 0.189331 | 0.73123 | 0.733484 |
| 6 | 0.408352 | 0.40757 | 0.402246 | 0.407986 | 0.170097 | 0.192987 | 0.215755 | 0.213773 | 0.743865 | 0.745872 |
| 7 | 0.408352 | 0.40757 | 0.402362 | 0.408047 | 0.190324 | 0.215841 | 0.240812 | 0.236253 | 0.755651 | 0.757263 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.265637 | 0.264096 | 0.263457 | 0.260027 | 0.0713328 | 0.0730259 | 0.0746911 | 0.0780058 | 0.38401 | 0.381504 |
| 1 | 0.266365 | 0.265695 | 0.265606 | 0.261874 | 0.079737 | 0.0835761 | 0.0874313 | 0.0905882 | 0.391674 | 0.390139 |
| 2 | 0.266469 | 0.266017 | 0.266919 | 0.262805 | 0.0920443 | 0.0981407 | 0.104103 | 0.106644 | 0.400779 | 0.399711 |
| 3 | 0.266481 | 0.266073 | 0.267723 | 0.263115 | 0.105174 | 0.113417 | 0.121031 | 0.122825 | 0.409636 | 0.408784 |
| 4 | 0.266481 | 0.266081 | 0.268802 | 0.263275 | 0.118098 | 0.128306 | 0.137274 | 0.138208 | 0.417954 | 0.417117 |
| 5 | 0.266481 | 0.266083 | 0.269537 | 0.26337 | 0.13043 | 0.142441 | 0.152709 | 0.152433 | 0.425696 | 0.424716 |
| 6 | 0.266481 | 0.266083 | 0.269655 | 0.263402 | 0.14205 | 0.155778 | 0.167047 | 0.165544 | 0.432875 | 0.431643 |
| 7 | 0.266481 | 0.266083 | 0.269682 | 0.263408 | 0.153052 | 0.16824 | 0.180358 | 0.177393 | 0.439517 | 0.437949 |
predictions_df_30
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.9579 | 0.9569 | 0.9517 | 0.9421 | 0.9299 | 0.9327 | 0.9291 | 0.9143 | 0.956 | 0.9528 |
| 1 | 0.954 | 0.9516 | 0.9041 | 0.9111 | 0.9329 | 0.9319 | 0.9247 | 0.9081 | 0.9452 | 0.9478 |
| 2 | 0.953 | 0.951 | 0.8884 | 0.903 | 0.9189 | 0.9154 | 0.9027 | 0.8784 | 0.925 | 0.9291 |
| 3 | 0.953 | 0.9509 | 0.8802 | 0.9022 | 0.8932 | 0.8906 | 0.8675 | 0.8391 | 0.9015 | 0.9075 |
| 4 | 0.953 | 0.9509 | 0.8725 | 0.9008 | 0.8608 | 0.8601 | 0.8268 | 0.7913 | 0.8819 | 0.8834 |
| 5 | 0.953 | 0.9509 | 0.8567 | 0.9001 | 0.8246 | 0.8234 | 0.7813 | 0.7329 | 0.8593 | 0.8623 |
| 6 | 0.953 | 0.9509 | 0.856 | 0.8998 | 0.7857 | 0.7852 | 0.7346 | 0.6782 | 0.8399 | 0.8372 |
| 7 | 0.953 | 0.9509 | 0.856 | 0.8997 | 0.7448 | 0.7466 | 0.6889 | 0.6239 | 0.8193 | 0.8112 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.40201 | 0.391454 | 0.365024 | 0.378182 | 0.0655538 | 0.067508 | 0.0716126 | 0.0861135 | 0.657567 | 0.659072 |
| 1 | 0.409904 | 0.406427 | 0.382609 | 0.395726 | 0.0772725 | 0.0819139 | 0.0897807 | 0.105963 | 0.674399 | 0.675981 |
| 2 | 0.410503 | 0.408983 | 0.390059 | 0.403515 | 0.0952452 | 0.103297 | 0.115343 | 0.131461 | 0.690095 | 0.692167 |
| 3 | 0.410528 | 0.409377 | 0.394017 | 0.406829 | 0.115633 | 0.1273 | 0.143244 | 0.158357 | 0.705024 | 0.707504 |
| 4 | 0.410529 | 0.409432 | 0.397393 | 0.40863 | 0.136808 | 0.15209 | 0.171209 | 0.185038 | 0.719212 | 0.721859 |
| 5 | 0.410529 | 0.409446 | 0.399795 | 0.409405 | 0.157707 | 0.176353 | 0.198546 | 0.210386 | 0.732596 | 0.735132 |
| 6 | 0.410529 | 0.409451 | 0.400378 | 0.40972 | 0.178159 | 0.199896 | 0.225659 | 0.233841 | 0.745156 | 0.747396 |
| 7 | 0.410529 | 0.409451 | 0.400521 | 0.409867 | 0.197545 | 0.22272 | 0.249986 | 0.255627 | 0.756881 | 0.758684 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.266435 | 0.26473 | 0.263247 | 0.260379 | 0.081913 | 0.0829286 | 0.0851656 | 0.0940992 | 0.38735 | 0.384617 |
| 1 | 0.267615 | 0.266616 | 0.265379 | 0.262522 | 0.0883726 | 0.0913939 | 0.0958708 | 0.105251 | 0.393387 | 0.391876 |
| 2 | 0.267711 | 0.267033 | 0.266672 | 0.263688 | 0.0992905 | 0.104545 | 0.111273 | 0.120127 | 0.401957 | 0.40098 |
| 3 | 0.267712 | 0.267098 | 0.267532 | 0.264126 | 0.111414 | 0.118854 | 0.127536 | 0.135363 | 0.410548 | 0.409816 |
| 4 | 0.267712 | 0.267106 | 0.268595 | 0.264334 | 0.123636 | 0.13317 | 0.143293 | 0.150039 | 0.41871 | 0.418011 |
| 5 | 0.267712 | 0.267108 | 0.269206 | 0.264453 | 0.135414 | 0.146832 | 0.158284 | 0.163657 | 0.426357 | 0.425513 |
| 6 | 0.267712 | 0.26711 | 0.269328 | 0.264512 | 0.146658 | 0.159833 | 0.172749 | 0.176066 | 0.43348 | 0.432357 |
| 7 | 0.267712 | 0.26711 | 0.269362 | 0.264536 | 0.157131 | 0.172197 | 0.185608 | 0.187455 | 0.440098 | 0.43861 |
predictions_df_40
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.9286 | 0.9355 | 0.9319 | 0.9194 | 0.8924 | 0.894 | 0.8934 | 0.8694 | 0.9451 | 0.9428 |
| 1 | 0.9299 | 0.9305 | 0.8811 | 0.8928 | 0.9001 | 0.8988 | 0.8909 | 0.8598 | 0.9357 | 0.9389 |
| 2 | 0.9297 | 0.9296 | 0.866 | 0.885 | 0.8852 | 0.8789 | 0.8614 | 0.8248 | 0.9181 | 0.9224 |
| 3 | 0.9297 | 0.9295 | 0.8588 | 0.8827 | 0.8645 | 0.8512 | 0.8273 | 0.781 | 0.8958 | 0.8992 |
| 4 | 0.9297 | 0.9295 | 0.8504 | 0.8814 | 0.8315 | 0.8191 | 0.7885 | 0.7259 | 0.8724 | 0.8779 |
| 5 | 0.9297 | 0.9295 | 0.839 | 0.881 | 0.7944 | 0.7837 | 0.7375 | 0.6743 | 0.852 | 0.857 |
| 6 | 0.9297 | 0.9295 | 0.8387 | 0.8805 | 0.7593 | 0.7463 | 0.6957 | 0.6251 | 0.8295 | 0.8321 |
| 7 | 0.9297 | 0.9295 | 0.8387 | 0.8802 | 0.7201 | 0.7038 | 0.6565 | 0.5677 | 0.8066 | 0.8122 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.402065 | 0.38873 | 0.359412 | 0.374456 | 0.0817783 | 0.0835792 | 0.0893766 | 0.120947 | 0.659909 | 0.66194 |
| 1 | 0.412575 | 0.40705 | 0.37987 | 0.394187 | 0.0922067 | 0.0964227 | 0.105899 | 0.139457 | 0.677104 | 0.678895 |
| 2 | 0.413643 | 0.410571 | 0.388267 | 0.403303 | 0.109051 | 0.116376 | 0.130318 | 0.164275 | 0.692663 | 0.694841 |
| 3 | 0.413706 | 0.411189 | 0.392883 | 0.407578 | 0.128452 | 0.139374 | 0.157354 | 0.190461 | 0.707296 | 0.709866 |
| 4 | 0.413709 | 0.411278 | 0.396449 | 0.409941 | 0.14864 | 0.162956 | 0.184909 | 0.216084 | 0.721191 | 0.723912 |
| 5 | 0.413709 | 0.411286 | 0.398828 | 0.411019 | 0.168841 | 0.18653 | 0.211371 | 0.240408 | 0.734347 | 0.736905 |
| 6 | 0.413709 | 0.411287 | 0.399568 | 0.41146 | 0.188507 | 0.209535 | 0.236577 | 0.262834 | 0.746753 | 0.748851 |
| 7 | 0.413709 | 0.411287 | 0.399744 | 0.411684 | 0.207486 | 0.231597 | 0.260627 | 0.283335 | 0.758428 | 0.759857 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.267698 | 0.2654 | 0.263535 | 0.261213 | 0.0936516 | 0.0945045 | 0.0975511 | 0.115138 | 0.391233 | 0.388439 |
| 1 | 0.269343 | 0.267494 | 0.266027 | 0.263642 | 0.0985905 | 0.101272 | 0.106443 | 0.124693 | 0.395577 | 0.39409 |
| 2 | 0.269529 | 0.268031 | 0.267292 | 0.265106 | 0.108289 | 0.112985 | 0.120652 | 0.138465 | 0.40355 | 0.402624 |
| 3 | 0.269541 | 0.268144 | 0.268174 | 0.265706 | 0.119491 | 0.126324 | 0.136036 | 0.152765 | 0.411798 | 0.411111 |
| 4 | 0.269541 | 0.268163 | 0.269114 | 0.265978 | 0.130904 | 0.139738 | 0.151265 | 0.166493 | 0.419726 | 0.419061 |
| 5 | 0.269541 | 0.268165 | 0.26965 | 0.266136 | 0.14206 | 0.152851 | 0.165599 | 0.179334 | 0.427212 | 0.426369 |
| 6 | 0.269541 | 0.268165 | 0.269798 | 0.266211 | 0.152735 | 0.165392 | 0.179029 | 0.191051 | 0.43424 | 0.433049 |
| 7 | 0.269541 | 0.268165 | 0.269839 | 0.266262 | 0.162852 | 0.177259 | 0.191638 | 0.201624 | 0.440815 | 0.439154 |
predictions_df_50
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.8886 | 0.9013 | 0.9005 | 0.8932 | 0.8549 | 0.8501 | 0.8444 | 0.8171 | 0.9279 | 0.9278 |
| 1 | 0.8915 | 0.8953 | 0.8522 | 0.8645 | 0.8648 | 0.8543 | 0.8376 | 0.8072 | 0.9243 | 0.9259 |
| 2 | 0.8913 | 0.8943 | 0.8376 | 0.8566 | 0.8499 | 0.8368 | 0.8098 | 0.7747 | 0.9035 | 0.9085 |
| 3 | 0.8914 | 0.8943 | 0.8309 | 0.8536 | 0.8228 | 0.8072 | 0.7694 | 0.7298 | 0.8828 | 0.8835 |
| 4 | 0.8914 | 0.8943 | 0.8238 | 0.852 | 0.7888 | 0.7755 | 0.729 | 0.6744 | 0.8596 | 0.8615 |
| 5 | 0.8914 | 0.8943 | 0.8153 | 0.8507 | 0.7538 | 0.7393 | 0.6881 | 0.6199 | 0.8395 | 0.8399 |
| 6 | 0.8914 | 0.8943 | 0.8152 | 0.8499 | 0.7172 | 0.7041 | 0.6466 | 0.5676 | 0.8195 | 0.8192 |
| 7 | 0.8914 | 0.8943 | 0.8152 | 0.8492 | 0.682 | 0.6651 | 0.6064 | 0.5218 | 0.8002 | 0.7965 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.404547 | 0.388145 | 0.355368 | 0.371496 | 0.0996954 | 0.101952 | 0.119126 | 0.189564 | 0.663194 | 0.665695 |
| 1 | 0.41807 | 0.409956 | 0.377992 | 0.394078 | 0.108782 | 0.113559 | 0.135789 | 0.208704 | 0.680711 | 0.68282 |
| 2 | 0.419471 | 0.414477 | 0.387368 | 0.404558 | 0.124522 | 0.132098 | 0.159161 | 0.232202 | 0.696009 | 0.698491 |
| 3 | 0.41956 | 0.415359 | 0.392621 | 0.409467 | 0.142962 | 0.153597 | 0.185206 | 0.256812 | 0.710305 | 0.713272 |
| 4 | 0.419562 | 0.415513 | 0.396436 | 0.412502 | 0.162224 | 0.176127 | 0.211652 | 0.280807 | 0.72382 | 0.727139 |
| 5 | 0.419563 | 0.415532 | 0.398942 | 0.413933 | 0.181475 | 0.198786 | 0.237343 | 0.30346 | 0.73659 | 0.740001 |
| 6 | 0.419563 | 0.415534 | 0.399799 | 0.414552 | 0.200372 | 0.221268 | 0.261529 | 0.325053 | 0.748646 | 0.751882 |
| 7 | 0.419563 | 0.415534 | 0.400022 | 0.414947 | 0.218378 | 0.242679 | 0.284769 | 0.344019 | 0.760004 | 0.762828 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.270287 | 0.267307 | 0.264952 | 0.262616 | 0.105995 | 0.106965 | 0.116014 | 0.15242 | 0.395963 | 0.392933 |
| 1 | 0.272464 | 0.269693 | 0.26781 | 0.265496 | 0.109453 | 0.112477 | 0.124262 | 0.16141 | 0.398502 | 0.397029 |
| 2 | 0.272731 | 0.270321 | 0.26911 | 0.26725 | 0.117968 | 0.122914 | 0.137297 | 0.173835 | 0.405758 | 0.404942 |
| 3 | 0.272748 | 0.27049 | 0.270018 | 0.267933 | 0.128202 | 0.135055 | 0.151782 | 0.186901 | 0.413574 | 0.413105 |
| 4 | 0.272748 | 0.270521 | 0.270856 | 0.268319 | 0.138832 | 0.147622 | 0.16623 | 0.199509 | 0.421178 | 0.420852 |
| 5 | 0.272748 | 0.270525 | 0.271387 | 0.268548 | 0.149291 | 0.160042 | 0.179969 | 0.211271 | 0.428384 | 0.428036 |
| 6 | 0.272748 | 0.270526 | 0.271556 | 0.268671 | 0.159375 | 0.172172 | 0.192712 | 0.22232 | 0.435167 | 0.434638 |
| 7 | 0.272748 | 0.270526 | 0.271602 | 0.26875 | 0.16885 | 0.183579 | 0.204782 | 0.23195 | 0.441542 | 0.440678 |
predictions_df_60
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.8367 | 0.8472 | 0.8507 | 0.8458 | 0.7888 | 0.794 | 0.7829 | 0.7457 | 0.9058 | 0.8999 |
| 1 | 0.8419 | 0.843 | 0.8051 | 0.8192 | 0.8047 | 0.8026 | 0.7804 | 0.7281 | 0.906 | 0.9073 |
| 2 | 0.8409 | 0.842 | 0.7912 | 0.8099 | 0.789 | 0.7823 | 0.7521 | 0.6908 | 0.8842 | 0.8892 |
| 3 | 0.8409 | 0.8423 | 0.7851 | 0.8053 | 0.7639 | 0.7565 | 0.7156 | 0.6464 | 0.8646 | 0.8668 |
| 4 | 0.8409 | 0.8425 | 0.7796 | 0.8033 | 0.7331 | 0.7215 | 0.6796 | 0.5922 | 0.8415 | 0.8456 |
| 5 | 0.8409 | 0.8425 | 0.7742 | 0.8027 | 0.7024 | 0.6839 | 0.6355 | 0.5464 | 0.8213 | 0.821 |
| 6 | 0.8409 | 0.8425 | 0.7741 | 0.8025 | 0.6649 | 0.648 | 0.5966 | 0.5043 | 0.7976 | 0.801 |
| 7 | 0.8409 | 0.8425 | 0.7741 | 0.8024 | 0.6308 | 0.6129 | 0.5557 | 0.4634 | 0.7778 | 0.7792 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.40765 | 0.388622 | 0.351771 | 0.369919 | 0.122039 | 0.126166 | 0.165388 | 0.287639 | 0.667369 | 0.670564 |
| 1 | 0.427041 | 0.41591 | 0.377085 | 0.395794 | 0.129919 | 0.136762 | 0.181192 | 0.306055 | 0.685583 | 0.6882 |
| 2 | 0.428843 | 0.422289 | 0.387553 | 0.408811 | 0.144255 | 0.153891 | 0.203111 | 0.328218 | 0.700852 | 0.703803 |
| 3 | 0.429025 | 0.423586 | 0.393211 | 0.415273 | 0.161309 | 0.173901 | 0.227722 | 0.351252 | 0.714941 | 0.718307 |
| 4 | 0.429031 | 0.423821 | 0.397461 | 0.419155 | 0.179454 | 0.195076 | 0.252844 | 0.373813 | 0.728282 | 0.731806 |
| 5 | 0.429031 | 0.423847 | 0.400114 | 0.420831 | 0.197445 | 0.216456 | 0.277437 | 0.395017 | 0.740881 | 0.744291 |
| 6 | 0.429031 | 0.42385 | 0.401098 | 0.42137 | 0.215076 | 0.23751 | 0.300842 | 0.414595 | 0.752775 | 0.755853 |
| 7 | 0.429031 | 0.42385 | 0.401378 | 0.421584 | 0.231962 | 0.257941 | 0.323384 | 0.432573 | 0.763976 | 0.766536 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.274055 | 0.270886 | 0.267108 | 0.265315 | 0.120676 | 0.122518 | 0.142482 | 0.204069 | 0.401584 | 0.39843 |
| 1 | 0.277572 | 0.273914 | 0.270653 | 0.269 | 0.122883 | 0.12688 | 0.149657 | 0.212075 | 0.402309 | 0.400985 |
| 2 | 0.277939 | 0.274881 | 0.272176 | 0.271311 | 0.130116 | 0.135986 | 0.161371 | 0.22324 | 0.408879 | 0.40826 |
| 3 | 0.277975 | 0.275122 | 0.273005 | 0.272308 | 0.139208 | 0.146944 | 0.174702 | 0.235059 | 0.416326 | 0.416002 |
| 4 | 0.277977 | 0.275169 | 0.273759 | 0.272818 | 0.148924 | 0.158515 | 0.188184 | 0.24656 | 0.423688 | 0.42342 |
| 5 | 0.277977 | 0.275175 | 0.274236 | 0.273043 | 0.158484 | 0.170084 | 0.201158 | 0.257293 | 0.430716 | 0.430317 |
| 6 | 0.277977 | 0.275175 | 0.274419 | 0.273099 | 0.167742 | 0.181332 | 0.213374 | 0.267149 | 0.437372 | 0.436694 |
| 7 | 0.277977 | 0.275175 | 0.27447 | 0.27311 | 0.176523 | 0.192131 | 0.22502 | 0.276159 | 0.443644 | 0.44256 |
predictions_df_70
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.7681 | 0.7736 | 0.7863 | 0.7806 | 0.7172 | 0.7169 | 0.7033 | 0.6482 | 0.8749 | 0.8705 |
| 1 | 0.7754 | 0.7804 | 0.7457 | 0.7644 | 0.7308 | 0.7211 | 0.6959 | 0.6232 | 0.8796 | 0.8755 |
| 2 | 0.7746 | 0.7801 | 0.7312 | 0.7571 | 0.7155 | 0.7056 | 0.6686 | 0.5829 | 0.8574 | 0.8577 |
| 3 | 0.7746 | 0.7799 | 0.7275 | 0.7552 | 0.6947 | 0.6786 | 0.63 | 0.5398 | 0.8394 | 0.8358 |
| 4 | 0.7746 | 0.7799 | 0.7234 | 0.7525 | 0.6638 | 0.6502 | 0.5933 | 0.4903 | 0.8166 | 0.8153 |
| 5 | 0.7746 | 0.7799 | 0.7185 | 0.7515 | 0.6263 | 0.62 | 0.5587 | 0.45 | 0.7958 | 0.7966 |
| 6 | 0.7746 | 0.7799 | 0.7185 | 0.751 | 0.5949 | 0.5852 | 0.5281 | 0.4147 | 0.7792 | 0.7746 |
| 7 | 0.7746 | 0.7799 | 0.7187 | 0.7509 | 0.5646 | 0.5555 | 0.4964 | 0.3856 | 0.761 | 0.756 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.410797 | 0.390256 | 0.348375 | 0.368509 | 0.148181 | 0.153608 | 0.246155 | 0.466543 | 0.673333 | 0.676965 |
| 1 | 0.436226 | 0.423234 | 0.376743 | 0.396726 | 0.154913 | 0.163683 | 0.261233 | 0.484374 | 0.692575 | 0.695464 |
| 2 | 0.439251 | 0.43193 | 0.388703 | 0.412208 | 0.168067 | 0.17997 | 0.281876 | 0.504546 | 0.70782 | 0.710971 |
| 3 | 0.439418 | 0.433716 | 0.394774 | 0.419637 | 0.183648 | 0.198585 | 0.305455 | 0.525229 | 0.721559 | 0.725026 |
| 4 | 0.439422 | 0.43396 | 0.39943 | 0.42405 | 0.200329 | 0.218863 | 0.328992 | 0.545226 | 0.734477 | 0.73804 |
| 5 | 0.439423 | 0.433981 | 0.402412 | 0.425877 | 0.217076 | 0.238997 | 0.352158 | 0.564188 | 0.746643 | 0.750072 |
| 6 | 0.439423 | 0.433983 | 0.403699 | 0.426478 | 0.233655 | 0.259041 | 0.374713 | 0.581057 | 0.75809 | 0.761163 |
| 7 | 0.439423 | 0.433983 | 0.404121 | 0.426724 | 0.249807 | 0.278494 | 0.396594 | 0.596849 | 0.768821 | 0.771363 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.278267 | 0.274983 | 0.270121 | 0.268238 | 0.137213 | 0.139711 | 0.185964 | 0.295296 | 0.408737 | 0.40524 |
| 1 | 0.283003 | 0.2789 | 0.274463 | 0.272121 | 0.138302 | 0.143327 | 0.192304 | 0.302461 | 0.407669 | 0.406275 |
| 2 | 0.283638 | 0.280318 | 0.276318 | 0.274772 | 0.144398 | 0.151475 | 0.202942 | 0.312104 | 0.413383 | 0.412764 |
| 3 | 0.283676 | 0.280669 | 0.277108 | 0.275847 | 0.152326 | 0.161286 | 0.215362 | 0.322313 | 0.420254 | 0.419943 |
| 4 | 0.283677 | 0.280715 | 0.277881 | 0.276384 | 0.160975 | 0.172052 | 0.227747 | 0.33223 | 0.427182 | 0.426934 |
| 5 | 0.283677 | 0.28072 | 0.278367 | 0.276644 | 0.169683 | 0.182742 | 0.239806 | 0.341618 | 0.433857 | 0.433492 |
| 6 | 0.283677 | 0.28072 | 0.278595 | 0.27675 | 0.178264 | 0.193292 | 0.251437 | 0.349923 | 0.440175 | 0.439547 |
| 7 | 0.283677 | 0.28072 | 0.278676 | 0.276784 | 0.186555 | 0.20345 | 0.262574 | 0.35766 | 0.446124 | 0.445117 |
predictions_df_80
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.676 | 0.6844 | 0.6944 | 0.6818 | 0.6367 | 0.6278 | 0.597 | 0.5299 | 0.8292 | 0.8287 |
| 1 | 0.6906 | 0.702 | 0.6648 | 0.6822 | 0.6451 | 0.629 | 0.599 | 0.5033 | 0.8314 | 0.8318 |
| 2 | 0.6908 | 0.7024 | 0.6556 | 0.68 | 0.6381 | 0.6111 | 0.575 | 0.4774 | 0.8159 | 0.8185 |
| 3 | 0.6907 | 0.7023 | 0.6531 | 0.6788 | 0.6206 | 0.5896 | 0.5382 | 0.4376 | 0.7965 | 0.7996 |
| 4 | 0.6907 | 0.7023 | 0.6507 | 0.6777 | 0.5888 | 0.5672 | 0.5092 | 0.3987 | 0.7792 | 0.778 |
| 5 | 0.6907 | 0.7023 | 0.6488 | 0.6774 | 0.5636 | 0.5368 | 0.4805 | 0.3643 | 0.7614 | 0.7583 |
| 6 | 0.6907 | 0.7023 | 0.6489 | 0.6765 | 0.539 | 0.5088 | 0.4554 | 0.3327 | 0.7416 | 0.7388 |
| 7 | 0.6907 | 0.7023 | 0.6489 | 0.6763 | 0.5089 | 0.4844 | 0.4256 | 0.3109 | 0.7239 | 0.7203 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.417814 | 0.393367 | 0.348816 | 0.370509 | 0.18009 | 0.188496 | 0.384405 | 0.773584 | 0.681764 | 0.686335 |
| 1 | 0.450517 | 0.433106 | 0.379292 | 0.401461 | 0.185615 | 0.198562 | 0.399041 | 0.79183 | 0.702655 | 0.70583 |
| 2 | 0.454228 | 0.443733 | 0.392422 | 0.418621 | 0.197017 | 0.213236 | 0.417627 | 0.80865 | 0.718311 | 0.721202 |
| 3 | 0.454544 | 0.445724 | 0.398698 | 0.42669 | 0.210987 | 0.230243 | 0.438798 | 0.825761 | 0.731824 | 0.734809 |
| 4 | 0.454552 | 0.445979 | 0.403285 | 0.431569 | 0.225775 | 0.248417 | 0.460401 | 0.842244 | 0.744445 | 0.747266 |
| 5 | 0.454552 | 0.446002 | 0.40654 | 0.433399 | 0.240855 | 0.267129 | 0.481661 | 0.857966 | 0.756367 | 0.758691 |
| 6 | 0.454552 | 0.446004 | 0.408108 | 0.433915 | 0.255625 | 0.285987 | 0.50176 | 0.872566 | 0.767566 | 0.769196 |
| 7 | 0.454552 | 0.446004 | 0.408649 | 0.434149 | 0.270112 | 0.304591 | 0.521461 | 0.886265 | 0.778067 | 0.77888 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.284672 | 0.280654 | 0.275078 | 0.272587 | 0.156653 | 0.160321 | 0.257476 | 0.448744 | 0.417861 | 0.414172 |
| 1 | 0.291017 | 0.285196 | 0.280224 | 0.276573 | 0.156628 | 0.163584 | 0.26326 | 0.455895 | 0.41516 | 0.413564 |
| 2 | 0.291834 | 0.286853 | 0.282653 | 0.279414 | 0.161386 | 0.170532 | 0.27246 | 0.463481 | 0.420138 | 0.419119 |
| 3 | 0.29191 | 0.287241 | 0.283586 | 0.280512 | 0.168068 | 0.17918 | 0.283355 | 0.471558 | 0.426499 | 0.42563 |
| 4 | 0.291912 | 0.287294 | 0.284215 | 0.281066 | 0.175408 | 0.188597 | 0.29452 | 0.479468 | 0.433032 | 0.432078 |
| 5 | 0.291912 | 0.287299 | 0.284681 | 0.281299 | 0.182981 | 0.1983 | 0.30543 | 0.487048 | 0.439405 | 0.438171 |
| 6 | 0.291912 | 0.2873 | 0.284943 | 0.28138 | 0.190422 | 0.208022 | 0.315679 | 0.494089 | 0.445487 | 0.443825 |
| 7 | 0.291912 | 0.2873 | 0.285054 | 0.281429 | 0.197676 | 0.217566 | 0.325619 | 0.500684 | 0.451232 | 0.449058 |
predictions_df_90
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.5741 | 0.5789 | 0.5924 | 0.5796 | 0.5376 | 0.5347 | 0.4992 | 0.4003 | 0.7381 | 0.7441 |
| 1 | 0.5912 | 0.6028 | 0.5694 | 0.5992 | 0.5512 | 0.5361 | 0.4989 | 0.3624 | 0.7545 | 0.7551 |
| 2 | 0.5932 | 0.6035 | 0.5638 | 0.603 | 0.5427 | 0.5239 | 0.4792 | 0.3388 | 0.7427 | 0.7464 |
| 3 | 0.5929 | 0.6035 | 0.5609 | 0.602 | 0.5243 | 0.5034 | 0.4553 | 0.3155 | 0.7249 | 0.7335 |
| 4 | 0.5929 | 0.6035 | 0.5588 | 0.5998 | 0.505 | 0.4853 | 0.4297 | 0.2941 | 0.7071 | 0.7146 |
| 5 | 0.5929 | 0.6035 | 0.5579 | 0.5989 | 0.4796 | 0.4677 | 0.4046 | 0.2688 | 0.6858 | 0.6973 |
| 6 | 0.5929 | 0.6035 | 0.558 | 0.5982 | 0.457 | 0.4453 | 0.3859 | 0.2502 | 0.671 | 0.6789 |
| 7 | 0.5929 | 0.6035 | 0.558 | 0.5982 | 0.4383 | 0.4249 | 0.371 | 0.2352 | 0.6556 | 0.6638 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.425812 | 0.398201 | 0.350201 | 0.372566 | 0.220935 | 0.245757 | 0.649353 | 1.31741 | 0.693606 | 0.699147 |
| 1 | 0.464508 | 0.443792 | 0.382918 | 0.404827 | 0.22597 | 0.256416 | 0.664977 | 1.33298 | 0.716716 | 0.720447 |
| 2 | 0.468334 | 0.457096 | 0.397021 | 0.423436 | 0.236609 | 0.269858 | 0.681133 | 1.34565 | 0.733068 | 0.736146 |
| 3 | 0.468613 | 0.459656 | 0.403164 | 0.432388 | 0.249656 | 0.285689 | 0.698919 | 1.35867 | 0.746445 | 0.749317 |
| 4 | 0.468633 | 0.459996 | 0.407728 | 0.438209 | 0.263084 | 0.302162 | 0.717598 | 1.37166 | 0.758506 | 0.761129 |
| 5 | 0.468634 | 0.460029 | 0.411233 | 0.44049 | 0.276701 | 0.318654 | 0.736029 | 1.38326 | 0.769638 | 0.771899 |
| 6 | 0.468635 | 0.460031 | 0.413078 | 0.441093 | 0.290003 | 0.336094 | 0.753106 | 1.39419 | 0.78003 | 0.781762 |
| 7 | 0.468635 | 0.460032 | 0.413717 | 0.441347 | 0.302931 | 0.353041 | 0.769905 | 1.40523 | 0.789749 | 0.790846 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.291348 | 0.286751 | 0.280529 | 0.276974 | 0.180427 | 0.191823 | 0.390611 | 0.717463 | 0.429491 | 0.425428 |
| 1 | 0.298661 | 0.292086 | 0.28653 | 0.280939 | 0.179755 | 0.195017 | 0.39666 | 0.723347 | 0.425312 | 0.4236 |
| 2 | 0.299456 | 0.294244 | 0.289442 | 0.283862 | 0.183718 | 0.201023 | 0.404397 | 0.728795 | 0.429423 | 0.42837 |
| 3 | 0.299516 | 0.294775 | 0.290429 | 0.285069 | 0.189605 | 0.208751 | 0.413308 | 0.734735 | 0.435124 | 0.434172 |
| 4 | 0.299517 | 0.294854 | 0.291068 | 0.285694 | 0.195975 | 0.217013 | 0.422738 | 0.740791 | 0.441048 | 0.439984 |
| 5 | 0.299517 | 0.294862 | 0.291598 | 0.28603 | 0.202559 | 0.225404 | 0.432033 | 0.746235 | 0.446843 | 0.445533 |
| 6 | 0.299517 | 0.294862 | 0.291927 | 0.286137 | 0.209033 | 0.234247 | 0.440638 | 0.751377 | 0.452389 | 0.450725 |
| 7 | 0.299517 | 0.294862 | 0.292042 | 0.286178 | 0.215344 | 0.242821 | 0.449046 | 0.756612 | 0.457632 | 0.455547 |
predictions_df_100
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.4823 | 0.4839 | 0.4906 | 0.4733 | 0.4349 | 0.4429 | 0.3895 | 0.2745 | 0.6428 | 0.6443 |
| 1 | 0.4973 | 0.515 | 0.4706 | 0.5043 | 0.4428 | 0.4409 | 0.3792 | 0.233 | 0.6565 | 0.6557 |
| 2 | 0.4975 | 0.5168 | 0.4664 | 0.5121 | 0.4357 | 0.424 | 0.3651 | 0.2179 | 0.6453 | 0.6528 |
| 3 | 0.4976 | 0.517 | 0.466 | 0.5138 | 0.4229 | 0.4052 | 0.3492 | 0.2083 | 0.6272 | 0.6373 |
| 4 | 0.4976 | 0.517 | 0.4641 | 0.511 | 0.4059 | 0.3892 | 0.3304 | 0.1957 | 0.6177 | 0.6229 |
| 5 | 0.4976 | 0.517 | 0.4635 | 0.5105 | 0.3908 | 0.3789 | 0.3162 | 0.1853 | 0.6026 | 0.6093 |
| 6 | 0.4976 | 0.517 | 0.4635 | 0.5104 | 0.3731 | 0.363 | 0.3051 | 0.1753 | 0.586 | 0.5968 |
| 7 | 0.4976 | 0.517 | 0.4635 | 0.5103 | 0.3583 | 0.3488 | 0.2947 | 0.1648 | 0.5751 | 0.5868 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.436829 | 0.407245 | 0.356266 | 0.379787 | 0.280696 | 0.341972 | 1.09486 | 2.00171 | 0.710982 | 0.717736 |
| 1 | 0.482005 | 0.458285 | 0.391727 | 0.414688 | 0.286548 | 0.356475 | 1.11107 | 2.01262 | 0.737424 | 0.741281 |
| 2 | 0.486998 | 0.473787 | 0.406676 | 0.435306 | 0.295102 | 0.370187 | 1.12433 | 2.01951 | 0.754701 | 0.757179 |
| 3 | 0.487398 | 0.476682 | 0.412737 | 0.445237 | 0.305669 | 0.385362 | 1.13925 | 2.02665 | 0.767848 | 0.769833 |
| 4 | 0.487408 | 0.477064 | 0.417276 | 0.451614 | 0.317304 | 0.40188 | 1.15463 | 2.03374 | 0.779417 | 0.780985 |
| 5 | 0.487408 | 0.477116 | 0.420977 | 0.454004 | 0.328882 | 0.418724 | 1.16897 | 2.04073 | 0.78999 | 0.791073 |
| 6 | 0.487408 | 0.477123 | 0.422943 | 0.454522 | 0.340263 | 0.434641 | 1.18278 | 2.04709 | 0.79973 | 0.800222 |
| 7 | 0.487408 | 0.477123 | 0.423602 | 0.454657 | 0.351226 | 0.451215 | 1.19685 | 2.05348 | 0.808793 | 0.808585 |
| Over_dim_iteration 256 10_Targets | Over_dim_iteration 128 10_Targets | Over_dim_iteration 64 10_Targets | Over_dim_iteration 32 10_Targets | Over_dim_iteration 256 Mnist | Over_dim_iteration 128 Mnist | Over_dim_iteration 64 Mnist | Over_dim_iteration 32 Mnist | Over_dim_iteration 256 Noisy | Over_dim_iteration 128 Noisy | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.299818 | 0.294712 | 0.28758 | 0.283684 | 0.213528 | 0.242457 | 0.611274 | 1.05384 | 0.444565 | 0.440164 |
| 1 | 0.308448 | 0.30089 | 0.294478 | 0.288183 | 0.212619 | 0.247261 | 0.617602 | 1.0578 | 0.43963 | 0.437413 |
| 2 | 0.309511 | 0.303408 | 0.297675 | 0.291512 | 0.215195 | 0.253088 | 0.623779 | 1.06051 | 0.443046 | 0.441268 |
| 3 | 0.309608 | 0.303962 | 0.298687 | 0.292871 | 0.219559 | 0.260254 | 0.631147 | 1.06358 | 0.448056 | 0.446258 |
| 4 | 0.30961 | 0.304037 | 0.299357 | 0.29356 | 0.2248 | 0.268253 | 0.63882 | 1.06677 | 0.453358 | 0.451387 |
| 5 | 0.30961 | 0.304048 | 0.299932 | 0.293908 | 0.230128 | 0.27652 | 0.645998 | 1.06996 | 0.45858 | 0.45633 |
| 6 | 0.30961 | 0.304049 | 0.300279 | 0.293984 | 0.235426 | 0.284405 | 0.652874 | 1.0729 | 0.46359 | 0.460975 |
| 7 | 0.30961 | 0.30405 | 0.300412 | 0.293997 | 0.24055 | 0.292587 | 0.659833 | 1.07587 | 0.468375 | 0.465303 |